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Compute pearson product-moment correlation coefficients of two given NumPy arrays

Last Updated : 02 Sep, 2020
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In NumPy, We can compute pearson product-moment correlation coefficients of two given arrays with the help of numpy.corrcoef() function.

In this function, we will pass arrays as a parameter and it will return the pearson product-moment correlation coefficients of two given arrays.

Syntax: numpy.corrcoef(x, y=None, rowvar=True, bias=, ddof=)
Return: Pearson product-moment correlation coefficients

Let’s see an example:

Example 1:

Python




# import library
import numpy as np
  
# create numpy 1d-array
array1 = np.array([0, 1, 2])
array2 = np.array([3, 4, 5])
  
# pearson product-moment correlation
# coefficients of the arrays
rslt = np.corrcoef(array1, array2)
  
print(rslt)


Output

[[1. 1.]
 [1. 1.]]

Example 2:

Python




# import numpy library
import numpy as np
  
# create a numpy 1d-array
array1 = np.array([ 2, 4, 8])
array2 = np.array([ 3, 2,1])
  
  
# pearson product-moment correlation
# coefficients of the arrays
rslt2 = np.corrcoef(array1, array2)
  
print(rslt2)


Output

[[ 1.         -0.98198051]
 [-0.98198051  1.        ]]

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